Which of these breakthroughs will still matter in 20 years?

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One thing that becomes clear if you pay close attention to science is that for all the rigor, discipline, and hard work that is required to make big discoveries, something completely unscientific also plays an important role: serendipity.

Despite efforts to place funding bets wisely and support projects that are likely to have the most impact outside the laboratory, progress often comes where people least expect it. That is one of the marvelous things about the whole enterprise: People carefully design experiments based on their expectations, and yet surprises occur.

Some scientists even argue the whole reason to do experiments is to be astonished; if we could predict how everything would come out, it would be far easier to cure disease or predict human behavior.

Even so, a new field is increasingly focused on using science to understand science. Can researchers develop more powerful tools to distinguish high-impact findings from those that are quickly forgotten? Is there a better way to identify which scientists or research areas should receive limited funding?

In a paper published in the journal Science on Thursday, a team from Northeastern University took a shot at creating a new tool for predicting whether a paper will be a major breakthrough.

In the new work, Albert-László Barabási, a physicist who works in the emerging field of analyzing networks, found that with four to five years of data on how a paper has been received by the scientific community, it is possible to make a fairly good prediction of the long-term influence.

He used a barometer called “fitness,” which is a quantitative measure of how the community responds to a new piece of science.

In an accompanying piece, James Evans, a sociologist from the University of Chicago, praised the research but also warned of its limits. A tool that identifies research that will be important in the long run could, he argues, actually lead to self-fulfilling prophecies: research that becomes important long-term in part because of the prediction.